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Self-organizing networks for classification: developing applications to science analysis for astroparticle physics

Author

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  • De Angelis, A.
  • Boinee, P.
  • Frailis, M.
  • Milotti, E.

Abstract

Physics analysis in astroparticle experiments requires the capability of recognizing new phenomena; in order to establish what is new, it is important to develop tools for automatic classification, able to compare the final result with data from different detectors. A typical example is the problem of gamma ray burst detection, classification, and possible association to known sources: for this task physicists will need in the next years tools to associate data from optical databases, from satellite experiments (EGRET, GLAST), and from Cherenkov telescopes (MAGIC, HESS, CANGAROO, VERITAS).

Suggested Citation

  • De Angelis, A. & Boinee, P. & Frailis, M. & Milotti, E., 2004. "Self-organizing networks for classification: developing applications to science analysis for astroparticle physics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 338(1), pages 50-53.
    • Handle: RePEc:eee:phsmap:v:338:y:2004:i:1:p:50-53
    DOI: 10.1016/j.physa.2004.02.023
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